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第35届中国控制会议

作者： MENG Xiangrui WANG Shuo CAO Zhiqiang ZHANG Leijie State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781509009107

As legged robots are suitable to be used in unstructured environments,it becomes a popular field of research *** this paper,the development of quadruped robots is *** several typical and recent robot systems are addressed in details,such as HyQ series,StarlETH,ANYmal,MIT Cheetah and BigDog,***,some key techniques of environment perception for quadruped robots,including sensors,feature extraction and identification,mapping and SLAM,are also ***,future researches of quadruped robots in environment perception are given.

关键词： Quadruped robot environment perception sensors feature extraction mapping and SLAM

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Move Like Humans:End-to-end Gaussian Process Regression Based Target Tracking control for Mobile Robots 36

Move Like Humans:End-to-end Gaussian Process Regression Base...

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第36届中国控制会议

作者： Ning An Shi-Ying Sun Xiao-Guang Zhao Zeng-Guang Hou State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781538629185

In this paper,we address the problem of target tracking control for mobile robots with limited sensing *** end-to-end Gaussian process regression learning control method is proposed to transfer the human control experiences to the *** end-to-end learning architecture directly learns the control mapping from the original sensing input space to the final control output space in an human-like *** non-parametric Gaussian process regression accurately transfers the complex human control experiences to the control *** addition,realistic training data set are collected from human operators for the end-to-end control *** performance of the proposed target tracking control method is extensively evaluated on various real-world scenarios,experimental results have demonstrated the robustness,accuracy,and effectiveness of the proposed method.

关键词： Target Tracking control End-to-end Learning Human-like control Non-parametric Bayesian Model Gaussian Process Regression

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Research on Astronomical Trajectory Planning Algorithm for FAST 29

Research on Astronomical Trajectory Planning Algorithm for F...

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第29届中国控制与决策会议

作者： Sai Deng Fengshui Jing Guodong Yang Zize Liang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

ISBN: (纸本)9781509046584

This paper addresses astronomical trajectory planning algorithms for tracking and basket-weaving for Five-hundred-meter Aperture Spherical Radio Telescope(FAST).In order to achieve stable operation at feed receiver of FAST and to ease difficulties of trajectory planning in 3-D space,the observed trajectories were planned in celestial coordinate ***,the characteristics of tracking and basket-weaving were analyzed,and the involved parameters for tracking and basket-weaving were ***,the constraint conditions for planning parameters corresponding to the practical motion were obtained;besides trajectory planning algorithms based on double S velocity profile for tracking and basket-weaving were ***,the numerical results indicated that the planned trajectories are guaranteed to be continuous in position,velocity and acceleration,as well as respect the given motion constraint *** the proposed algorithm cost less planning time than the algorithm plans in 3-D space.

关键词： trajectory planning FAST telescope double S

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Cascade Motion Planning for Vibration Suppression of the focus Cabin in FAST 36

Cascade Motion Planning for Vibration Suppression of the Foc...

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第36届中国控制会议

作者： Zheng Rongzhang Jing Fengshui Yang Guodong University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

The Five-hundred-meter Aperture Spherical Radio Telescope（FAST） requires high accuracy of positioning and attitude *** to its flexible structure,the cable-cabin system can be excited to vibrate,which affects the *** alleviate the vibration during the slewing task,taking advantage of s-curve planning and input shaping,a cascade motion planning method is *** the main vibration mode,a simplified cable-cabin model is built for simulation according to the parameters of *** experiments are conducted to prove that the cascade method is effective in vibration suppression and robust to the change of natural frequency.

关键词： FAST telescope vibration suppression motion planning s-curve input shaping

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Investigating the dynamic memory effect of human drivers via ON-LSTM

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Science China(Information Sciences) 2020年第9期63卷 83-93页

作者： Shengzhe DAI Zhiheng LI Li LI Dongpu CAO Xingyuan DAI Yilun LIN Department of Automation Tsinghua University Shenzhen International Graduate School Tsinghua University Department of Mechanical and Mechatronics Engineering University of Waterloo State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

It is a widely accepted view that considering the memory effects of historical information(driving operations) is beneficial for vehicle trajectory prediction models to improve prediction accuracy. However,many commonly used models(e.g., long short-term memory, LSTM) can only implicitly simulate memory effects, but lack effective mechanisms to capture memory effects from sequence data and estimate their effective time range(ETR). This shortage makes it hard to dynamically configure the most suitable length of used historical information according to the current driving behavior, which harms the good understanding of vehicle motion. To address this problem, we propose a modified trajectory prediction model based on ordered neuron LSTM(ON-LSTM). We demonstrate the feasibility of ETR estimation based on ON-LSTM and propose an ETR estimation method. We estimate the ETR of driving fluctuations and lane change operations on the NGSIM I-80 dataset. The experiment results prove that the proposed method can well capture the memory effects during trajectory prediction. Moreover, the estimated ETR values are in agreement with our intuitions.

关键词： driving behavior memory effect trajectory prediction historical information ON-LSTM

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Accurate and Robust Eye Center Localization via Fully Convolutional Networks

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IEEE/CAA Journal of Automatica Sinica 2019年第5期6卷 1127-1138页

作者： Yifan Xia Hui Yu Fei-Yue Wang the School of Creative Technologies University of PortsmouthPortsmouthPO1 2DJUK the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network(FCN),which is an end-to-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by *** experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

关键词： Deep learning eye center localization eye gaze estimation eye tracking fully convolutional network (FCN) humancomputer interaction

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A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

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Machine Intelligence Research 2022年第4期19卷 331-346页

作者： Song Zhang Nan Zheng Dan-Li Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Due to the popularity of group activities in social media,group recommendation becomes increasingly *** aims to pursue a list of preferred items for a target *** deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and ***,these methods may suffer from data sparsity *** for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and *** interactions,which take place in the range of a group,form a local view of a certain *** addition to local information,groups with common interests may also show similar tastes on ***,group representation can be conducted according to the similarity among groups,which forms a global view of a certain *** this paper,we propose a novel global and local information fusion neural network(GLIF)model for group *** GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local ***,our model also leverages ANN to obtain a group′s global representation based on the similarity among different ***,it fuses global and local representations based on attention mechanism to form a group′s comprehensive ***,group recommendation is conducted under neural collaborative filtering(NCF)*** experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

关键词： Group recommendation attentive neural network(ANN) global information local information recommender system

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A Novel forwarding Method for Full-Duplex Two-Way Relay Networks

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China Communications 2017年第4期14卷 65-72页

作者： Shuangshuang Han Xiang Cheng Liuqing Yang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing China and Qingdao Academy of Intelligent Industries The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Department of Electrical and Computer Engineering Colorado State University

An estimate-and-forward(EF) scheme for single-input single-output(SISO) and multiple-input multiple-output(MIMO) full-duplex two-way relay networks is proposed and analyzed. The relay estimates the received signal from two terminal nodes by a minimum mean squared error(MMSE) estimation and forwards a scaled version of the MMSE estimate to the destination. The proposed EF outperforms conventional amplify-and-forward(AF) and decode-and-forward(DF) across all signal-to-noise ratio(SNR) region. Because its computational complexity is high for relays with a large number of antennas(large MIMO) and/or high order constellations, an approximate EF scheme, called list EF, are thus proposed to reduce the computational complexity. The proposed list EF computes a candidate list for the MMSE estimate by using a sphere decoder, and it approaches the performance of the exact EF relay at a negligible performance loss. The proposed forwarding approach also could be used to other relay networks, such as half-duplex, one-way or massive MIMO relay networks.

关键词： estimate-and-forward full-duplex two-way relay wireless communications

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Design and 3D Motion Modeling of a 300-m Gliding Robotic Dolphin

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IFAC-PapersOnLine 2017年第1期50卷 12685-12690页

作者： Yuan, Jun Wu, Zhengxing Yu, Junzhi Zhou, Chao Tan, Min University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

This paper presents the mechatronic design and three-dimensional (3D) motion modeling of a gliding robotic dolphin capable of diving as deep as 300 m. Propulsion modes of bio-inspired robotic dolphin and traditional underwater gliders are integrated into the robot in order to endow it with both high maneuverability and long endurance simultaneously. It is realized by introducing a buoyancy adjusting mechanism on the basis of a bio-inspired robotic dolphin. Particular design on mechanical seal and compressive strength is implemented to achieve large diving depth. Further, a full-state dynamic model for 3D gliding motion is established for motion analysis. In comparison with traditional underwater gliders, the robot's flippers and fluke are controllable and thereby enrich the state-regulating modes of gliding motion, which is considered specially in the dynamic model. Simulations are conducted to analyze the robot's 3D gliding motion. Experiments are also carried out to validate the robot's gliding performance and the effectiveness of the formulated dynamic model. © 2017

关键词： 3D modeling Biomimetics Compressive strength Dolphins (structures) Dynamic models Dynamics Machine design Motion control Robotics Robots Adjusting mechanism Bio inspired robotics Mechatronic design Robotic dolphin state regulating Three dimensional (3D) motion Underwater gliders Underwater robotics

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Convection-UNet: A Deep Convolutional Neural Network for Convection Detection based on the Geo High-speed Imager of Fengyun-4B

Convection-UNet: A Deep Convolutional Neural Network for Con...

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2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Wang, Yufei Xiao, Baihua Chinese Academy of Sciences University of Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9798350346596

Deep convection can cause a variety of severe weather conditions such as thunderstorms, strong winds, and heavy rainfall. Satellite observations provide all-weather and multi-directional observations, facilitating the timely detection of such weather systems, which is crucial to saving lives and property. However, previous methods based on channel feature extraction and threshold filtering did not make full use of information in satellite images, which led to limitations on such complex problems as strong convection detection. In this study, we propose a novel framework of a deep learning-based model Convection-UNet to detect convection. We use channel 4 to 7 of FY-4B GHI that we select according to the microphysical properties of convection as input and radar reflectivity as label. We combine the detailed training time and test time data augmentation strategies and build a deep neural network to automatically extract spatial context features and achieve end-to-end learning. Results show that the performance of our method far exceeds the previous channel extraction combined with threshold filtering methods such as BT and BTD at least 0.24 on Fi-measure. We also show that our channel selection and data augmentation strategies are of great significance to detect convection. © 2023 IEEE.

关键词： Feature extraction

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